Title
Spectral clustering for feature-based metric maps partitioning in a hybrid mapping framework
Abstract
Hybrid maps combine metric and topological information for efficiently managing large-scale environments. In a feature-based mapping framework, this paper describes the application of a spectral clustering approach for automatically detecting the transitions between subsequently traversed local maps. Contrary to recently proposed approaches, this algorithm considers each individual map feature as a node of a graph whose edges link two nodes if they are simultaneously observed. Thus, given a sequence of observations, an auxiliary graph is incrementally built whose edges carry non-negative weights according to the locality of the features. Given a feature, its locality defines the set of features that has been observed simultaneously with it at least once. At each execution of the mapping approach, the feature-based graph is split into two subgraphs using a normalized spectral clustering algorithm. If the graph partition is validated, the algorithm determines that the robot is moving into a new area and a new local map is generated. We have tested the proposed approach in real environments where features are obtained using 2D laser sensors or vision. Experimental results demonstrate the performance of the proposal.
Year
DOI
Venue
2009
10.1109/ROBOT.2009.5152476
ICRA
Keywords
Field
DocType
spectral clustering approach,mapping approach,graph partition,feature-based graph,hybrid mapping framework,feature-based metric map,individual map feature,hybrid map,edges link,feature-based mapping framework,auxiliary graph,simultaneous localization and mapping,graph partitioning,clustering algorithms,spectral clustering,information management,graph theory,environmental management,path planning,mobile robots,navigation
Spectral clustering,Locality,Control theory,Computer science,Metric map,Theoretical computer science,Artificial intelligence,Simultaneous localization and mapping,Cluster analysis,Graph partition,Graph theory,Motion planning,Pattern recognition
Conference
Volume
Issue
ISSN
2009
1
1050-4729
ISBN
Citations 
PageRank 
978-1-4244-2789-5
0
0.34
References 
Authors
16
5
Name
Order
Citations
PageRank
R. Vázquez-Martín1778.51
Pedro Núñez2717.50
A. Bandera316023.70
Francisco Sandoval4203.48
Vazquez-Martin, R.500.34